diff --git a/docs/configuration.md b/docs/configuration.md
index c39b4890851bc..0a650e76b7c40 100644
--- a/docs/configuration.md
+++ b/docs/configuration.md
@@ -678,9 +678,10 @@ Apart from these, the following properties are also available, and may be useful
   <td><code>spark.rdd.compress</code></td>
   <td>false</td>
   <td>
-    Whether to compress serialized RDD partitions (e.g. for
-    <code>StorageLevel.MEMORY_ONLY_SER</code>). Can save substantial space at the cost of some
-    extra CPU time.
+    Whether to compress serialized RDD partitions (e.g. for 
+    <code>StorageLevel.MEMORY_ONLY_SER</code> in Java 
+    and Scala or <code>StorageLevel.MEMORY_ONLY</code> in Python). 
+    Can save substantial space at the cost of some extra CPU time. 
   </td>
 </tr>
 <tr>
diff --git a/docs/programming-guide.md b/docs/programming-guide.md
index f823b89a4b5e9..c5e2a1cd7b8aa 100644
--- a/docs/programming-guide.md
+++ b/docs/programming-guide.md
@@ -1196,14 +1196,14 @@ storage levels is:
     partitions that don't fit on disk, and read them from there when they're needed. </td>
 </tr>
 <tr>
-  <td> MEMORY_ONLY_SER </td>
+  <td> MEMORY_ONLY_SER <br /> (Java and Scala) </td>
   <td> Store RDD as <i>serialized</i> Java objects (one byte array per partition).
     This is generally more space-efficient than deserialized objects, especially when using a
     <a href="tuning.html">fast serializer</a>, but more CPU-intensive to read.
   </td>
 </tr>
 <tr>
-  <td> MEMORY_AND_DISK_SER </td>
+  <td> MEMORY_AND_DISK_SER <br /> (Java and Scala) </td>
   <td> Similar to MEMORY_ONLY_SER, but spill partitions that don't fit in memory to disk instead of
     recomputing them on the fly each time they're needed. </td>
 </tr>
@@ -1230,7 +1230,9 @@ storage levels is:
 </tr>
 </table>
 
-**Note:** *In Python, stored objects will always be serialized with the [Pickle](https://docs.python.org/2/library/pickle.html) library, so it does not matter whether you choose a serialized level.*
+**Note:** *In Python, stored objects will always be serialized with the [Pickle](https://docs.python.org/2/library/pickle.html) library, 
+so it does not matter whether you choose a serialized level. The available storage levels in Python include `MEMORY_ONLY`, `MEMORY_ONLY_2`, 
+`MEMORY_AND_DISK`, `MEMORY_AND_DISK_2`, `DISK_ONLY`, `DISK_ONLY_2` and `OFF_HEAP`.*
 
 Spark also automatically persists some intermediate data in shuffle operations (e.g. `reduceByKey`), even without users calling `persist`. This is done to avoid recomputing the entire input if a node fails during the shuffle. We still recommend users call `persist` on the resulting RDD if they plan to reuse it.
 
@@ -1243,7 +1245,7 @@ efficiency. We recommend going through the following process to select one:
   This is the most CPU-efficient option, allowing operations on the RDDs to run as fast as possible.
 
 * If not, try using `MEMORY_ONLY_SER` and [selecting a fast serialization library](tuning.html) to
-make the objects much more space-efficient, but still reasonably fast to access.
+make the objects much more space-efficient, but still reasonably fast to access. (Java and Scala)
 
 * Don't spill to disk unless the functions that computed your datasets are expensive, or they filter
 a large amount of the data. Otherwise, recomputing a partition may be as fast as reading it from
diff --git a/python/pyspark/rdd.py b/python/pyspark/rdd.py
index 4b4d59647b2bc..5f21c2d9a9dcc 100644
--- a/python/pyspark/rdd.py
+++ b/python/pyspark/rdd.py
@@ -220,18 +220,18 @@ def context(self):
 
     def cache(self):
         """
-        Persist this RDD with the default storage level (C{MEMORY_ONLY_SER}).
+        Persist this RDD with the default storage level (C{MEMORY_ONLY}).
         """
         self.is_cached = True
-        self.persist(StorageLevel.MEMORY_ONLY_SER)
+        self.persist(StorageLevel.MEMORY_ONLY)
         return self
 
-    def persist(self, storageLevel=StorageLevel.MEMORY_ONLY_SER):
+    def persist(self, storageLevel=StorageLevel.MEMORY_ONLY):
         """
         Set this RDD's storage level to persist its values across operations
         after the first time it is computed. This can only be used to assign
         a new storage level if the RDD does not have a storage level set yet.
-        If no storage level is specified defaults to (C{MEMORY_ONLY_SER}).
+        If no storage level is specified defaults to (C{MEMORY_ONLY}).
 
         >>> rdd = sc.parallelize(["b", "a", "c"])
         >>> rdd.persist().is_cached
diff --git a/python/pyspark/sql/dataframe.py b/python/pyspark/sql/dataframe.py
index 746bb55e14f22..add92add0f0f1 100644
--- a/python/pyspark/sql/dataframe.py
+++ b/python/pyspark/sql/dataframe.py
@@ -371,18 +371,18 @@ def foreachPartition(self, f):
 
     @since(1.3)
     def cache(self):
-        """ Persists with the default storage level (C{MEMORY_ONLY_SER}).
+        """ Persists with the default storage level (C{MEMORY_ONLY}).
         """
         self.is_cached = True
         self._jdf.cache()
         return self
 
     @since(1.3)
-    def persist(self, storageLevel=StorageLevel.MEMORY_ONLY_SER):
+    def persist(self, storageLevel=StorageLevel.MEMORY_ONLY):
         """Sets the storage level to persist its values across operations
         after the first time it is computed. This can only be used to assign
         a new storage level if the RDD does not have a storage level set yet.
-        If no storage level is specified defaults to (C{MEMORY_ONLY_SER}).
+        If no storage level is specified defaults to (C{MEMORY_ONLY}).
         """
         self.is_cached = True
         javaStorageLevel = self._sc._getJavaStorageLevel(storageLevel)
diff --git a/python/pyspark/storagelevel.py b/python/pyspark/storagelevel.py
index 676aa0f7144aa..d4f184a85d764 100644
--- a/python/pyspark/storagelevel.py
+++ b/python/pyspark/storagelevel.py
@@ -23,8 +23,10 @@ class StorageLevel(object):
     """
     Flags for controlling the storage of an RDD. Each StorageLevel records whether to use memory,
     whether to drop the RDD to disk if it falls out of memory, whether to keep the data in memory
-    in a serialized format, and whether to replicate the RDD partitions on multiple nodes.
-    Also contains static constants for some commonly used storage levels, such as MEMORY_ONLY.
+    in a JAVA-specific serialized format, and whether to replicate the RDD partitions on multiple
+    nodes. Also contains static constants for some commonly used storage levels, MEMORY_ONLY.
+    Since the data is always serialized on the Python side, all the constants use the serialized
+    formats.
     """
 
     def __init__(self, useDisk, useMemory, useOffHeap, deserialized, replication=1):
@@ -49,12 +51,21 @@ def __str__(self):
 
 StorageLevel.DISK_ONLY = StorageLevel(True, False, False, False)
 StorageLevel.DISK_ONLY_2 = StorageLevel(True, False, False, False, 2)
-StorageLevel.MEMORY_ONLY = StorageLevel(False, True, False, True)
-StorageLevel.MEMORY_ONLY_2 = StorageLevel(False, True, False, True, 2)
-StorageLevel.MEMORY_ONLY_SER = StorageLevel(False, True, False, False)
-StorageLevel.MEMORY_ONLY_SER_2 = StorageLevel(False, True, False, False, 2)
-StorageLevel.MEMORY_AND_DISK = StorageLevel(True, True, False, True)
-StorageLevel.MEMORY_AND_DISK_2 = StorageLevel(True, True, False, True, 2)
-StorageLevel.MEMORY_AND_DISK_SER = StorageLevel(True, True, False, False)
-StorageLevel.MEMORY_AND_DISK_SER_2 = StorageLevel(True, True, False, False, 2)
+StorageLevel.MEMORY_ONLY = StorageLevel(False, True, False, False)
+StorageLevel.MEMORY_ONLY_2 = StorageLevel(False, True, False, False, 2)
+StorageLevel.MEMORY_AND_DISK = StorageLevel(True, True, False, False)
+StorageLevel.MEMORY_AND_DISK_2 = StorageLevel(True, True, False, False, 2)
 StorageLevel.OFF_HEAP = StorageLevel(False, False, True, False, 1)
+
+"""
+.. note:: The following four storage level constants are deprecated in 2.0, since the records \
+will always be serialized in Python.
+"""
+StorageLevel.MEMORY_ONLY_SER = StorageLevel.MEMORY_ONLY
+""".. note:: Deprecated in 2.0, use ``StorageLevel.MEMORY_ONLY`` instead."""
+StorageLevel.MEMORY_ONLY_SER_2 = StorageLevel.MEMORY_ONLY_2
+""".. note:: Deprecated in 2.0, use ``StorageLevel.MEMORY_ONLY_2`` instead."""
+StorageLevel.MEMORY_AND_DISK_SER = StorageLevel.MEMORY_AND_DISK
+""".. note:: Deprecated in 2.0, use ``StorageLevel.MEMORY_AND_DISK`` instead."""
+StorageLevel.MEMORY_AND_DISK_SER_2 = StorageLevel.MEMORY_AND_DISK_2
+""".. note:: Deprecated in 2.0, use ``StorageLevel.MEMORY_AND_DISK_2`` instead."""
diff --git a/python/pyspark/streaming/context.py b/python/pyspark/streaming/context.py
index 1388b6d044e04..3deed52be0be2 100644
--- a/python/pyspark/streaming/context.py
+++ b/python/pyspark/streaming/context.py
@@ -258,7 +258,7 @@ def checkpoint(self, directory):
         """
         self._jssc.checkpoint(directory)
 
-    def socketTextStream(self, hostname, port, storageLevel=StorageLevel.MEMORY_AND_DISK_SER_2):
+    def socketTextStream(self, hostname, port, storageLevel=StorageLevel.MEMORY_AND_DISK_2):
         """
         Create an input from TCP source hostname:port. Data is received using
         a TCP socket and receive byte is interpreted as UTF8 encoded ``\\n`` delimited
diff --git a/python/pyspark/streaming/dstream.py b/python/pyspark/streaming/dstream.py
index acec850f02c2d..3d5d4c34b8ef8 100644
--- a/python/pyspark/streaming/dstream.py
+++ b/python/pyspark/streaming/dstream.py
@@ -208,10 +208,10 @@ def func(iterator):
     def cache(self):
         """
         Persist the RDDs of this DStream with the default storage level
-        (C{MEMORY_ONLY_SER}).
+        (C{MEMORY_ONLY}).
         """
         self.is_cached = True
-        self.persist(StorageLevel.MEMORY_ONLY_SER)
+        self.persist(StorageLevel.MEMORY_ONLY)
         return self
 
     def persist(self, storageLevel):
diff --git a/python/pyspark/streaming/flume.py b/python/pyspark/streaming/flume.py
index b3d1905365925..b1fff0a5c7d6b 100644
--- a/python/pyspark/streaming/flume.py
+++ b/python/pyspark/streaming/flume.py
@@ -40,7 +40,7 @@ class FlumeUtils(object):
 
     @staticmethod
     def createStream(ssc, hostname, port,
-                     storageLevel=StorageLevel.MEMORY_AND_DISK_SER_2,
+                     storageLevel=StorageLevel.MEMORY_AND_DISK_2,
                      enableDecompression=False,
                      bodyDecoder=utf8_decoder):
         """
@@ -70,7 +70,7 @@ def createStream(ssc, hostname, port,
 
     @staticmethod
     def createPollingStream(ssc, addresses,
-                            storageLevel=StorageLevel.MEMORY_AND_DISK_SER_2,
+                            storageLevel=StorageLevel.MEMORY_AND_DISK_2,
                             maxBatchSize=1000,
                             parallelism=5,
                             bodyDecoder=utf8_decoder):
diff --git a/python/pyspark/streaming/kafka.py b/python/pyspark/streaming/kafka.py
index cdf97ec73aaf9..13f8f9578e62a 100644
--- a/python/pyspark/streaming/kafka.py
+++ b/python/pyspark/streaming/kafka.py
@@ -40,7 +40,7 @@ class KafkaUtils(object):
 
     @staticmethod
     def createStream(ssc, zkQuorum, groupId, topics, kafkaParams=None,
-                     storageLevel=StorageLevel.MEMORY_AND_DISK_SER_2,
+                     storageLevel=StorageLevel.MEMORY_AND_DISK_2,
                      keyDecoder=utf8_decoder, valueDecoder=utf8_decoder):
         """
         Create an input stream that pulls messages from a Kafka Broker.
diff --git a/python/pyspark/streaming/mqtt.py b/python/pyspark/streaming/mqtt.py
index 1ce4093196e63..3a515ea4996f4 100644
--- a/python/pyspark/streaming/mqtt.py
+++ b/python/pyspark/streaming/mqtt.py
@@ -28,7 +28,7 @@ class MQTTUtils(object):
 
     @staticmethod
     def createStream(ssc, brokerUrl, topic,
-                     storageLevel=StorageLevel.MEMORY_AND_DISK_SER_2):
+                     storageLevel=StorageLevel.MEMORY_AND_DISK_2):
         """
         Create an input stream that pulls messages from a Mqtt Broker.